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Detecting morphological changes in the brain is important for a better understanding of normal aging and brain disorders. Growing and Adaptive MEshes (GAMEs) is a multi-dimensional method for modeling complex brain structures and highlight significant differences between groups, such as healthy subjects versus patients with Alzheimer disease (AD). In this work, we have extended the functionalities of GAMEs by introducing a multidimensional analysis of linear correlation between local morphological changes and other independent variables, thus allowing for epidemiological studies. The new algorithm has been validated on a challenging medical application: the correlation of morphological changes in hippocampi and thalamus with the MMSE score, in a population of healthy subjects and patients with AD.